from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/mt5-small-squad-qa")

# model prediction
answers = model.answer_q(list_question="What is a person called is practicing heresy?", list_context=" Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")

With transformers

python

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-small-squad-qa")
output = pipe("question: What is a person called is practicing heresy?, context: Heresy is any provocative belief or theory that is strongly at variance with established beliefs or customs. A heretic is a proponent of such claims or beliefs. Heresy is distinct from both apostasy, which is the explicit renunciation of one's religion, principles or cause, and blasphemy, which is an impious utterance or action concerning God or sacred things.")

Evaluation

Metric (Question Answering): raw metric file

	Score	Type	Dataset
AnswerExactMatch	55.51	default	lmqg/qg_squad
AnswerF1Score	70.14	default	lmqg/qg_squad
BERTScore	92.09	default	lmqg/qg_squad
Bleu_1	54.66	default	lmqg/qg_squad
Bleu_2	48.72	default	lmqg/qg_squad
Bleu_3	43.42	default	lmqg/qg_squad
Bleu_4	38.98	default	lmqg/qg_squad
METEOR	39.9	default	lmqg/qg_squad
MoverScore	82.04	default	lmqg/qg_squad
ROUGE_L	68.71	default	lmqg/qg_squad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_squad
dataset_name: default
input_types: ['paragraph_question']
output_types: ['answer']
prefix_types: None
model: google/mt5-small
max_length: 512
max_length_output: 32
epoch: 11
batch: 16
lr: 0.0005
fp16: False
random_seed: 1
gradient_accumulation_steps: 4
label_smoothing: 0.15

The full configuration can be found at fine-tuning config file.

Citation

@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}

lmqg/mt5-small-squad-qa

Model Card of lmqg/mt5-small-squad-qa

Overview

Usage

Evaluation

Training hyperparameters

Citation

lmqg/
mt5-small-squad-qa

Model Card of `lmqg/mt5-small-squad-qa`